Quantum Henry gas solubility optimization algorithm for global optimization

Mohammadi, Davood; Abd Elaziz, Mohamed; Moghdani, Reza; Demir, Emrah; Mirjalili, Seyedali

Abstract

This paper proposes an improvement on the recently introduced Henry Gas Solubility Optimization (HGSO) metaheuristic algorithm that simulates Henry’s gas law (i.e., the concentration of a gas sample in a liquid solvent is proportional to the concentration of the sample in the gas phase). As an improvement, we apply quantum theory instead of the standard procedure used in the HSGO algorithm for updating solutions. The proposed algorithm is named as Quantum HGSO (QHGSO) algorithm in this paper. The suggested changes enhance the ability of HGSO to create a counterbalance between exploitation and exploration for a better investigation of the solution space. For evaluating the capability of finding the optimal solution of our proposed algorithm, a collection of forty-seven global optimization functions is solved. Moreover, three well-known engineering problems are studied to show the performance of the QHGSO algorithm in constrained optimization problems. Comparative results with other well-known metaheuristic algorithms have shown that the QHGSO algorithm outperforms others with higher computational performance.

Citation

Mohammadi, D., Abd Elaziz, M., Moghdani, R., Demir, E., & Mirjalili, S. (2022). Quantum Henry gas solubility optimization algorithm for global optimization. Engineering with Computers, 38(Suppl. 3), 2329-2348. https://doi.org/10.1007/s00366-021-01347-1

• Journal Article Type: Article
• Acceptance Date: Feb 10, 2021
• Online Publication Date: Mar 27, 2021
• Publication Date: 2022-08
• Deposit Date: Feb 22, 2023
• Journal: Engineering with Computers
• Print ISSN: 0177-0667
• Electronic ISSN: 1435-5663
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 38
• Issue: Suppl. 3
• Pages: 2329-2348
• DOI: https://doi.org/10.1007/s00366-021-01347-1
• Keywords: Henry gas solubility optimization algorithm; Quantum theory; Metaheuristics; Optimization; Physics-based algorithms
• Public URL: https://hull-repository.worktribe.com/output/4210319
• Related Public URLs: https://orca.cardiff.ac.uk/id/eprint/138399/